[leftmargin=*]
We propose a hybridization of persistent [1]} and cooperative [2]} initialization to overcome limitations of each. The proposed method yields state-of-the-art FID scores for unconditional unnormalized image densities for the CIFAR-10 and ImageNet datasets. The ImagetNet results surpass GANs trained with similar resources. See Section .

We show that persistent initialization with appropriately tuned rejuvenation from in-distribution states can be used to train EBMs with stable trajectories of several thousand MCMC steps. This allows us to extend the method of [3]} to obtain state-of-the-art purification-based defense for CIFAR-10 and to scale the EBM defense to ImageNet. See Section .

We propose a method for principled density estimation that allows incorporation of a rejuvenation step for persistent states. Incorporating rejuvenation allows us to learn well-formed EBM densities at a greater scale than previously possible. See Section .

